• Higher mycophenolate dosage is associated with an increased risk of squamous cell carcinoma in kidney transplant recipients

      Shao, E. X.; Betz-Stablein, B.; Marquat, L.; Campbell, S.; Isbel, N.; Green, Adèle C; Plasmeijer, E. I.; Cancer and Population Studies, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Australiads. (2022)
      Background: Kidney transplant recipients are at increased risk of keratinocyte cancers, namely squamous cell and basal cell carcinomas (SCCs and BCCs). This is primarily due to the high levels of immunosuppression that are required to prevent allograft rejection. Different immunosuppressive medications confer different risks, and the effect of mycophenolate mofetil on SCC and BCC risk is unclear. We explored the relationship between mycophenolate dose prescribed over the entire transplant period and the risk of SCC and BCC. Methods: Kidney transplant recipients from Queensland, Australia, were recruited between 2012 and 2014 and followed until mid-2016. During this time transplant recipients underwent regular skin examinations to diagnose incident SCCs and BCCs. Immunosuppressive medication regimens were obtained from hospital records, and the average mycophenolate dose/day over the entire transplantation period was calculated for each patient. Doses were divided into three ranked groups, and adjusted relative risks (RRadj) of developing SCC and BCC tumours were calculated using negative binomial regression with the lowest dosage group as reference. Recipients who had used azathioprine previously were excluded; further sub-group analysis was performed for other immunosuppressant medications. Results: There were 134 kidney transplant recipients included in the study. The average age was 55, 31% were female and 69% were male. At the highest median mycophenolate dose of 1818 mg/day the SCC risk doubled (RRadj 2.22, 95% CI 1.03-4.77) when compared to the reference group of 1038 mg/day. An increased risk persisted after accounting for ever-use of ciclosporin, ever-use of tacrolimus, and when excluding mammalian target of rapamycin users. This increased risk was mainly carried by kidney transplant recipients immunosuppressed for five or more years (RRadj = 11.05 95% CI 2.50-48.81). In contrast, there was no significant association between BCC incidence and therapy with the highest compared with the lowest mycophenolate dosage (RRadj = 1.27 95% CI 0.56-2.87). Conclusion: Higher mycophenolate dosage is associated with increased SCCs in kidney transplant recipients, particularly those immunosuppressed for more than five years. The increased SCC risk persists after accounting for usage of other immunosuppressant medications.
    • A systematic review of clinical studies on variable proton Relative Biological Effectiveness (RBE)

      Underwood, Tracey; McNamara, A. L.; Appelt, A.; Haviland, J. S.; Singers Sørensen, B.; Troost, E. G. C.; Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK (2022)
      Recently, a number of clinical studies have explored links between possible Relative Biological Effectiveness (RBE) elevations and patient toxicities and/or image changes following proton therapy. Our objective was to perform a systematic review of such studies. We applied a "Problem [RBE], Intervention [Protons], Population [Patients], Outcome [Side effect]" search strategy to the PubMed database. From our search, we retrieved studies which: (a) performed novel voxel-wise analyses of patient effects versus physical dose and LET (n = 13), and (b) compared image changes between proton and photon cohorts with regard to proton RBE (n = 9). For each retrieved study, we extracted data regarding: primary tumour type; size of patient cohort; type of image change studied; image-registration method (deformable or rigid); LET calculation method, and statistical methodology. We compared and contrasted their methods in order to discuss the weight of clinical evidence for variable proton RBE. We concluded that clinical evidence for variable proton RBE remains statistically weak at present. Our principal recommendation is that proton centres and clinical trial teams collaborate to standardize follow-up protocols and statistical analysis methods, so that larger patient cohorts can ultimately be considered for RBE analyses.
    • Changes in body weight and serum cholesterol after heart transplant in relation to ventricular assist device implantation

      Miura, K.; Yu, R.; Entwistle, T. R.; McKenzie, S. C.; Green, Adèle C; Population Health Department, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia (2022)
      Weight gain is common after implantation of a ventricular assist device (VAD) prior to heart transplantation, but post-transplant changes in weight and also in blood lipids in those with VAD is virtually unknown. This study aimed to determine the influence of pre-transplant VAD implantation on body weight, blood cholesterol and triglyceride levels in Australian adult heart transplant recipients (HTRs), 1990-2017, from time of transplantation to 36 months post-transplantation. Information on VAD implantation, weight and blood lipids was collected for HTRs from medical records. Changes in weight and blood lipids from post-transplant to 12-, 24 and 36 months later, were assessed by VAD status using linear mixed-effects models. Of 236 heart transplant recipients, 48 (20%) had VAD implants. HTRs irrespective of VAD status, tended to increase their mean weight (p < 0.001) over 36 months (VAD implant: 76.9-84.4 kg; no VAD: 81.3-88.2 kg). Patients with VAD tended to have lower mean blood lipids but experienced increases similar to those with no VAD, from baseline to 36 months (cholesterol: VAD: 4.24-4.66 mmol/l; no VAD: 4.73-4.88 mmol/l; p = 0.05; triglycerides: VAD 1.59-1.63 mmol/l; no VAD 1.85-2.22 mmol/l; p = 0.09). We conclude that HTRs in general experience weight gain and lipid increases in the first 36 months after transplantation, regardless of prior VAD implantation.
    • Triplet therapy with androgen deprivation, docetaxel, and androgen receptor signalling inhibitors in metastatic castration-sensitive prostate cancer: A meta-analysis

      Ciccarese, C.; Iacovelli, R.; Sternberg, C. N.; Gillessen, Silke; Tortora, G.; Fizazi, K.; Medical Oncology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo Agostino Gemelli 8, 00168 Rome, Italy (2022)
      Background: The addition of either docetaxel or an androgen receptor signalling pathway inhibitor (ARSi) to androgen-deprivation therapy (ADT) has become the standard of care for metastatic castration-sensitive prostate cancer (mCSPC) patients. Recent phase III data support even greater survival impact of a triplet regimen with ADT plus docetaxel plus an ARSi (abiraterone or darolutamide) compared to ADT plus docetaxel. Objective: To evaluate whether the addition of an ARSi to ADT improves outcomes of mCSPC patients treated with docetaxel. Methods: We searched MEDLINE/PubMed, the Cochrane Library, and ASCO Meeting abstracts for randomised clinical trials (RCTs) testing the combination of ARSi + ADT in mCSPC men who received docetaxel. Data extraction was conducted according to the PRISMA statement. Summary hazard ratio (HR) was calculated using random- or fixed-effects models. The statistical analyses were performed with RevMan software (v.5.2.3). Results: Five RCTs were selected. Triplet therapy improved overall survival (OS) compared to ADT + docetaxel in mCSPC patients (HR = 0.73; p < 0.00001). This intensified strategy maintained the OS benefit when the ARSi was administered concomitant to chemotherapy (HR = 0.72; p < 0.00001), but no statistical effect was detected if the ARSi was sequential to docetaxel (p = 0.44). Moreover, in the subgroup of men with de novo mCSPC, triplets significantly improved OS (HR = 0.72, p < 0.0001). The lack of access to raw data was the main limit of our analysis. Conclusion: Our results support a clear survival advantage of adding an ARSi to ADT in mCSPC patients treated with docetaxel, mainly when the ARSi was administered concomitantly to chemotherapy and in the subgroup of de novo mCSPC.
    • Interplay of developmental hippo-notch signaling pathways with the DNA damage response in prostate cancer

      Mourkioti, I.; Angelopoulou, A.; Belogiannis, K.; Lagopati, N.; Potamianos, S.; Kyrodimos, E.; Gorgoulis, Vassilis G; Papaspyropoulos, A.; Molecular Carcinogenesis Group, Department of Histology and Embryology, Medical School, National Kapodistrian University of Athens (NKUA), 11527 Athens, Greece (2022)
      Prostate cancer belongs in the class of hormone-dependent cancers, representing a major cause of cancer incidence in men worldwide. Since upon disease onset almost all prostate cancers are androgen-dependent and require active androgen receptor (AR) signaling for their survival, the primary treatment approach has for decades relied on inhibition of the AR pathway via androgen deprivation therapy (ADT). However, following this line of treatment, cancer cell pools often become resistant to therapy, contributing to disease progression towards the significantly more aggressive castration-resistant prostate cancer (CRPC) form, characterized by poor prognosis. It is, therefore, of critical importance to elucidate the molecular mechanisms and signaling pathways underlying the progression of early-stage prostate cancer towards CRPC. In this review, we aim to shed light on the role of major signaling pathways including the DNA damage response (DDR) and the developmental Hippo and Notch pathways in prostate tumorigenesis. We recapitulate key evidence demonstrating the crosstalk of those pathways as well as with pivotal prostate cancer-related 'hubs' such as AR signaling, and evaluate the clinical impact of those interactions. Moreover, we attempt to identify molecules of the complex DDR-Hippo-Notch interplay comprising potentially novel therapeutic targets in the battle against prostate tumorigenesis.
    • Untangling the tumorigenic role of homotrimeric collagen I

      Jørgensen, Claus; Systems Oncology, Cancer Research UK Manchester Institute, University of Manchester, Alderley Park, Manchester, SK10 4TG, UK. (2022)
      Pancreatic ductal adenocarcinoma is characterized by a complex microenvironment. In this issue of Cancer Cell, Chen and colleagues define an oncogenic role of tumor-cell-produced collagen I homotrimers, wherein tumor development is promoted by integrin α3/β1-dependent activation of tumor cell signaling and modulation of tumor microbiome and immunity.
    • Clonal diversification and histogenesis of malignant germ cell tumours

      Oliver, T. R. W.; Chappell, L.; Sanghvi, R.; Deighton, L.; Ansari-Pour, N.; Dentro, S. C.; Young, M. D.; Coorens, T. H. H.; Jung, H.; Butler, T.; et al. (2022)
      Germ cell tumours (GCTs) are a collection of benign and malignant neoplasms derived from primordial germ cells. They are uniquely able to recapitulate embryonic and extraembryonic tissues, which carries prognostic and therapeutic significance. The developmental pathways underpinning GCT initiation and histogenesis are incompletely understood. Here, we study the relationship of histogenesis and clonal diversification in GCTs by analysing the genomes and transcriptomes of 547 microdissected histological units. We find no correlation between genomic and histological heterogeneity. However, we identify unifying features including the retention of fetal developmental transcripts across tissues, expression changes on chromosome 12p, and a conserved somatic evolutionary sequence of whole genome duplication followed by clonal diversification. While this pattern is preserved across all GCTs, the developmental timing of the duplication varies between prepubertal and postpubertal cases. In addition, tumours of younger children exhibit distinct substitution signatures which may lend themselves as potential biomarkers for risk stratification. Our findings portray the extensive diversification of GCT tissues and genetic subclones as randomly distributed, while identifying overarching transcriptional and genomic features.
    • Incorporating progesterone receptor expression into the PREDICT breast prognostic model

      Grootes, I.; Keeman, R.; Blows, F. M.; Milne, R. L.; Giles, G. G.; Swerdlow, A. J.; Fasching, P. A.; Abubakar, M.; Andrulis, I. L.; Anton-Culver, H.; et al. (2022)
      Background: Predict Breast (www.predict.nhs.uk) is an online prognostication and treatment benefit tool for early invasive breast cancer. The aim of this study was to incorporate the prognostic effect of progesterone receptor (PR) status into a new version of PREDICT and to compare its performance to the current version (2.2). Method: The prognostic effect of PR status was based on the analysis of data from 45,088 European patients with breast cancer from 49 studies in the Breast Cancer Association Consortium. Cox proportional hazard models were used to estimate the hazard ratio for PR status. Data from a New Zealand study of 11,365 patients with early invasive breast cancer were used for external validation. Model calibration and discrimination were used to test the model performance. Results: Having a PR-positive tumour was associated with a 23% and 28% lower risk of dying from breast cancer for women with oestrogen receptor (ER)-negative and ER-positive breast cancer, respectively. The area under the ROC curve increased with the addition of PR status from 0.807 to 0.809 for patients with ER-negative tumours (p = 0.023) and from 0.898 to 0.902 for patients with ER-positive tumours (p = 2.3 × 10-6) in the New Zealand cohort. Model calibration was modest with 940 observed deaths compared to 1151 predicted. Conclusion: The inclusion of the prognostic effect of PR status to PREDICT Breast has led to an improvement of model performance and more accurate absolute treatment benefit predictions for individual patients. Further studies should determine whether the baseline hazard function requires recalibration.
    • 3D Bioprinting: an enabling technology to understand melanoma

      Fernandes, S.; Vyas, C.; Lim, P.; Pereira, R. F.; Virós, Amaya; Bártolo, P.; Department of Mechanical, Aerospace and Civil Engineering, University of Manchester, Oxford Road, Manchester M13 9PL, UK (2022)
      Melanoma is a potentially fatal cancer with rising incidence over the last 50 years, associated with enhanced sun exposure and ultraviolet radiation. Its incidence is highest in people of European descent and the ageing population. There are multiple clinical and epidemiological variables affecting melanoma incidence and mortality, such as sex, ethnicity, UV exposure, anatomic site, and age. Although survival has improved in recent years due to advances in targeted and immunotherapies, new understanding of melanoma biology and disease progression is vital to improving clinical outcomes. Efforts to develop three-dimensional human skin equivalent models using biofabrication techniques, such as bioprinting, promise to deliver a better understanding of the complexity of melanoma and associated risk factors. These 3D skin models can be used as a platform for patient specific models and testing therapeutics.
    • Treatment time and circadian genotype interact to alter the severity of radiotherapy side-effects

      Talbot, C.; Webb, A.; Harper, E.; Azria, D.; Choudhury, Ananya; de Ruysscher, D.; Dunning, A.; Elliott, R.; Kerns, S.; Lambrecht, M.; et al. (2022)
      Purpose or Objective Circadian rhythm influences a wide range of biological processes, including efficacy and side effects of cancer treatment. Earlier evidence disagrees on whether risk of radiotherapy side-effects is affected by treatment time, probably due to differences in organs irradiated and time analysis methods. We previously showed an interactive effect of time and genotype of circadian rhythm genes on late toxicity after breast radiotherapy. This study aimed to validate those results in a larger multi-centre cohort with a more sophisticated time analysis and more SNPs. Materials and Methods We collected time of each radiotherapy fraction from patients in REQUITE breast cancer cohorts. Requite was a multi centre, prospective study in Europe and US (www.requite.eu). Enrolment was open for two and a half years through 26 centres in eight countries. Radiotherapy toxicity data was collected at baseline, after radiotherapy and one & two years later. Genome-wide SNP data was available typed with Illumina OncoArrays. The primary endpoints used were acute erythema and late breast atrophy assessed by CTCAE v4. 1690 breast cancer patients with complete clinical and SNP data were included in the analysis. Local date-times for each fraction were converted into solar times as continuous predictors. Genetic chronotype markers were included in logistic regression analyses to identify predictors of each end-point. Results Significant predictors for acute erythema included BMI, breast radiation dose and PER3 genotype. There was weak evidence for an effect of treatment time on acute toxicity, but with no interaction between time and genotype. In the late toxicity analysis BMI, breast radiation dose, surgery type, mean treatment time and SNPs in CLOCK (rs1801260), PER3 (rs2087947) and RASD1 (rs11545787) genes predicted late breast atrophy (p<0×05). There was a significant interaction between time and the genotypes of the circadian rhythm genes (p=0.005-0.02), with peak time for toxicity determined by genotype. Conclusion Late atrophy could be reduce by selecting the optimal treatment time based on the genotypes of circadian genes (Figure 1). For example, PER3 rs2087947C/C genotypes should be treated in the morning; T/T in the afternoon. We predict triple homozygous patients (who are 14% of this cohort) would reduce their chance of atrophy from 70% to 33% by treating in the morning instead of afternoon. Future clinical trials could stratify patients treated at optimal times compared to those scheduled conventionally to determine the magnitude of patient benefit.
    • Comparison of multiple gene expression platforms for measuring a bladder cancer hypoxia signature

      Smith, Tim A D; Lane, Brian; More, Elisabet; Valentine, Helen R; Lunj, Sapna; Abdelkarem, O. A.; Irlam-Jones, Joely J; Shabbir, Rekaya; Vora, Shrushti; Denley, H.; et al. (2022)
      Tumour hypoxia status provides prognostic information and predicts response to hypoxia‑modifying treatments. A previous study by our group derived a 24‑gene signature to assess hypoxia in bladder cancer. The objectives of the present study were to compare platforms for generating signature scores, identify cut‑off values for prospective studies, assess intra‑tumour heterogeneity and confirm hypoxia relevance. Briefly, RNA was extracted from prospectively collected diagnostic biopsies of muscle invasive bladder cancer (51 patients), and gene expression was measured using customised Taqman Low Density Array (TLDA) cards, NanoString and Clariom S arrays. Cross‑platform transferability of the gene signature was assessed using regression and concordance analysis. The cut‑off values were the cohort median expression values. Intra‑ and inter‑tumour variability were determined in a retrospective patient cohort (n=51) with multiple blocks (2‑18) from the same tumour. To demonstrate relevance, bladder cancer cell lines were exposed to hypoxia (0.1% oxygen, 24 h), and extracted RNA was run on custom TLDA cards. Hypoxia scores (HS) values showed good agreement between platforms: Clariom S vs. TLDA (r=0.72, P<0.0001; concordance 73%); Clariom S vs. NanoString (r=0.84, P<0.0001; 78%); TLDA vs. NanoString (r=0.80, P<0.0001; 78%). Cut‑off values were 0.047 (TLDA), 7.328 (NanoString) and 6.667 (Clariom S). Intra‑tumour heterogeneity in gene expression and HS (coefficient of variation 3.9%) was less than inter‑tumour (7.9%) variability. HS values were higher in bladder cancer cells exposed to hypoxia compared with normoxia (P<0.02). In conclusion, the present study revealed that application of the 24‑gene bladder cancer hypoxia signature was platform agnostic, cut‑off values determined prospectively can be used in a clinical trial, intra‑tumour heterogeneity was low and the signature was sensitive to changes in oxygen levels in vitro.
    • Determinants of fatigue and longitudinal changes up to 2 years post-radiotherapy for breast cancer

      Rosas, C.; Rattay, T.; Azria, D.; Elliott, Rebecca M; Gutierrez-Enriquez, S.; Rancati, T.; Rosenstein, B. S.; De Ruysscher, D.; Sperk, E.; Stobart, H.; et al. (2022)
      Purpose or Objective The aims of this study were to assess fatigue levels over time in breast cancer patients receiving radiotherapy (RT), and to identify demographic and treatment factors associated with multiple dimensions of long-term fatigue. Materials and Methods Data from breast cancer patients undergoing adjuvant RT after breast-conserving surgery was collected in a prospective international multicentre cohort study (www.requite.eu), including treatment/disease factors, baseline sociodemographic data and patient-reported outcomes. 31% of patients also received chemotherapy. Fatigue was measured at four time points (pre-RT, end of RT, 12 months and 24 months after RT) using the Multidimensional Fatigue Inventory (MFI-20) to assess general fatigue, physical fatigue, mental fatigue, reduced activity, and reduced motivation. The prevalence of moderate-severe fatigue was calculated using a cut-point of >12 (scale range 4-20). The change in fatigue levels over time was analyzed using Friedman test followed by pairwise Wilcoxon signed-rank test. Multivariable logistic regression models (including chemotherapy) were used to identify factors associated with the occurrence of fatigue two years after RT for every MFI-20 dimension. Results A total of 1443, 1302, and 1098 patients completed the MFI-20 at baseline, end of RT and two years after RT. The prevalence of moderate-severe general fatigue in our sample was 37%, 50%, and 34%, respectively. Patients with chemotherapy had higher baseline fatigue levels (Fig. 1). From baseline to the end of RT, levels of fatigue significantly increased for all MFI 20 dimensions (p-values <0.05). Fatigue levels had their peak at the end of RT and returned to baseline levels after two years for mental fatigue. For the other four MFI-20 dimensions, there was a statistically significant decrease in fatigue levels when comparing the baseline and two-year measurements (p-values <0.05). Baseline fatigue, depression, sleeping disorders, and dyspnea were significantly associated with the occurrence of general fatigue 2 years after RT (e.g., depression OR = 1.75, 95% CI 1.04-2.92), whereas overall quality of life was inversely associated (OR = 0.85, 95% CI 0.77- 0.94). In addition to the previously mentioned variables, other factors such as obesity or pain were associated with other fatigue domains. Conclusion Fatigue is a prevalent symptom in long-term breast cancer survivors receiving RT. Fatigue levels peaked by the end of RT and declined to baseline levels afterwards. Despite this overall decline, still a third of patients reported moderate-severe fatigue two years after RT. Several patient demographic factors and quality of life at baseline were associated with different dimensions of long-term fatigue. Screening for fatigue should be implemented in routine care to identify patients at a higher risk of developing long-term fatigue so that tailored interventions (e.g., psychological, exercise) can be offered in early phases.
    • Melanoma predilection for the lower limbs of women

      Shakeel, M.; Jiyad, Z.; Grant, Megan; Cook, M.; Green, Adèle C; Northern Care Alliance, Manchester, UK (2022)
      There is a clear predilection for cutaneous melanomas to occur on the lower limbs in women that is not understood. To assess this phenomenon in more detail than previously, we examined the distribution of primary melanomas on the lower limbs of females vs. males on different subsites and across age groups. We conducted a records-based study at an oncology hospital in north-west England and collected information on age at diagnosis, sex and anatomical subsite (thigh, lower leg, foot) from case notes and pathology reports of an unselected sample of 1522 patients with primary invasive melanoma treated between 2002 and 2015. A χ2-test was used to assess differences between categories, and multinomial logistic regression to calculate odds ratios (ORs) with 95% confidence intervals (CIs) for melanomas on limb subsites by age group in females vs. males. Of 316 patients with primary melanomas of the lower limb, 227 (72%) were in females: 55 on the thigh (F:M = 1.83), 142 on the lower leg (female : male ratio 3.74) and 30 on the feet (female : male ratio 1.15). Females aged < 40 years had 20 times the odds of males of presenting with thigh vs. foot melanomas (OR 20.0, 95% CI 2.6–152.6) and 7.5 times the odds of males aged < 40 of presenting with lower leg melanoma (OR 7.5, 95% CI 1.1–49.2). After the age of 40 years, odds of females developing thigh vs. foot melanomas were similar to the odds in males (OR 0.8) but corresponding odds of lower leg melanoma in females vs. males remained significantly increased (ORs 4.2 at the age of 40–59 years; OR 2.8 at ≥ 60 years of age). Results suggest females and males have higher prevalence of both genetic and environmental risk factors for lower limb melanomas overall, and also that the relative influences of these factors vary with anatomical subsite and age.
    • Predicting patient-reported symptom clusters in prostate cancer patients: A machine learning approach

      Rammant, E.; Deman, E.; Poppe, L.; Bultijnck, R.; Dirix, P.; De Meerleer, G.; Haustermans, K.; Van Hecke, A.; Azria, D.; Chang-Claude, J.; et al. (2022)
      Introduction & Objectives: Prostate cancer (PC) is the most common urological cancer in the world, with patients suffering from multiple co occurring symptoms (=symptom clusters (SC)). Identifying SC is important to anticipate on other symptoms within a SC and to uncover possibly overlooked symptoms. Also, supportive care interventions should aim to target multiple symptoms within a SC by addressing 1 or 2 symptoms and therefore alleviating the severity of other symptoms within that SC. This way, greater gains in a patients’ health-related quality of life (HRQoL) and more efficient patient care can be achieved. The aim of this study is to identify (1) SC and their changes over time in PC patients receiving radiotherapy (RT), (2) the impact of SC on HRQoL, and (3) demographic, clinical and, treatment-related predictors of SC. Materials & Methods: Data were used from REQUITE: an international prospective cohort study including PC patients receiving RT (26 hospitals, 8 countries). SC were identified based on patient-reported outcomes collected before RT(T1), end of RT(T2), month 12(T3), and month 24 after RT(T4) with the EORTC QLQ-C30 and pelvic symptom questionnaire. A combination of machine learning techniques were used to identify SC at different timepoints, to assess the impact of SC on HRQoL and to predict the SC, resp.: Hierarchical agglomerative clustering, multivariate linear regression and random forest regression. A first part of the dataset was used to develop the prediction model and a second part to validate the model for unseen data. Results: Data from 1538, 1490, 1322, and 1219 PC patients were analysed at T1, T2, T3 and T4, respectively. Three SC were identified at T1: SC1 (gastro-intestinal symptoms), SC2 (fatigue, urinary symptoms, emotional and cognitive functioning), and SC3 (pain, physical, role, and social functioning). At T2, changes in SC were seen: SC1 (gastro-intestinal symptoms), SC2 (fatigue, urinary problems, insomnia), SC3 (social and role functioning), and SC4 (pain, bowel problems, physical, emotional and cognitive functioning). At T3, SC returned to the 3 T1 SC and remained more or less stable at T4 (‘fatigue’ left SC2 and clustered together with ‘dyspnoea’ (SC4)). SC including ‘fatigue’ or ‘urinary symptoms’ had the highest frequencies across time-points. At T1, T3 and T4, cluster 2 and 3 (35-45% explained variance) had the strongest impact on the patients’ overall HRQoL. At T2, cluster 4 (52%) had the strongest impact. Planned RT target volume, PSA at prediagnostic biopsy, age and alcohol consumption were the best predictors of SC2 at T2 and SC3 and SC4 at T4. Conclusions: Several SC were identified in PC patients receiving RT. Although SC including fatigue and urinary symptoms were most common across time-points, the ‘pain, bowel problems, physical, emotional and cognitive functioning’ SC at T2 had the strongest impact on HRQoL. The predictors can be used to tailor future interventions.
    • Predicting patient-reported symptom clusters in lung cancer patients: a machine learning approach

      Rammant, E.; Deman, E.; Poppe, L.; Billiet, C.; Lambrecht, M.; Bultijnck, R.; Van Hecke, A.; Azria, D.; Chang-Claude, J.; Choudhury, Ananya; et al. (2022)
      Purpose or Objective Lung cancer is one of the most common cancer types in the world, with patients suffering from multiple co-occurring symptoms: i.e. ‘symptom clusters (SC)’. Identifying SC is important to anticipate on other symptoms within a cluster and to uncover possibly overlooked symptoms. Also, supportive care interventions should aim to target multiple symptoms within a SC by addressing 1 or 2 symptoms and therefore alleviating the severity of other symptoms within that SC. This way, greater gains in a patients’ health-related quality of life (HRQoL) can be achieved and patient care can be simplified. The aims of this study are to identify (1) SC and their change over time in lung cancer patients undergoing radiotherapy (RT), (2) SC with the greatest impact on HRQoL, and (3) demographical, clinical and/or treatment-related predictors of SC. Materials and Methods Data were used from the REQUITE study: an international prospective cohort study including lung cancer patients receiving RT from 26 different hospitals and 8 countries. SC were identified based on patient-reported outcomes collected before RT(T1), at month 3(T2), and month 6(T3) after RT with the EORTC QLQ-C3O and the lung symptom questionnaire. A combination of the following machine learning techniques were used to identify symptom clusters at different time-points, to investigate the impact of the SC on HRQoL and to predict the SC, respectively: hierarchical agglomerative clustering, linear regression and random forest regression. To guarantee external validity of the prediction model, a first part of the data set was used to develop the prediction model, and a second part to validate the prediction model for unseen data. Results Data from 418, 341, and 299 lung cancer patients were analysed at T1, T2, and T3, respectively. Three SC were identified and remained stable over time: cluster 1 (fatigue, dyspnoea, physical and role functioning), cluster 2 (coughing blood, swallowing problems, nausea and diarrhoea), and cluster 3 (social, emotional and cognitive functioning). On T1 and T2, a 4th cluster was identified (general pain, chest pain and coughing). Cluster 1 was most common across all time points, followed by clusters 3, 4 and 2. At T1, cluster 3 had the greatest impact on overall HRQoL (34% explained variance) while cluster 1 had the greatest impact at T2 (39%) and T3 (50%). Two symptoms within cluster 1 (dyspnoea and physical functioning) could be moderately predicted at T2 with age and RT parameters (i.e. planned target volume, max. dose oesophagus and dose per fraction) being the greatest predictors. Conclusion Supportive care interventions for lung cancer patients undergoing RT must tackle 1 or 2 symptoms of the ‘fatigue, dyspnoea, physical and role functioning’ cluster because this SC is most common across time-points and has the greatest impact on the patients’ HRQoL. Furthermore, age and RT parameters should be taken into account to further tailor future interventions in lung cancer patients.
    • How to design clinical trials which assess the advantage of new technologies

      Price, Gareth J; The University of Manchester, Manchester Cancer Research Centre, Manchester (2022)
      Abstract Text Much of the improvement in radiotherapy care witnessed seen over the last few decades has been driven by the clinical adoption of technical innovations. However, the well-known phase I-III randomised controlled trial framework used to assess new drug treatments is often not well suited to the evaluation of such innovations. As a result, many technical changes in radiotherapy practice are implemented without robust evidence of their impact on clinical outcome. In this presentation we use historic and contemporary examples to explore the need for such evidence, the reasons conventional clinical trials can be inappropriate for the evaluation of new techniques and technologies, and some of the approaches used and proposed to address this unmet need in clinical oncology. The talk will cover novel trial designs that are finding use in radiotherapy centres, the potential of pragmatic evaluations, and the advantages and disadvantages of observational studies, including prospective registries (e.g. MOMENTUM). We will introduce frameworks developed by investigators and regulatory bodies to draw on data from different study types when introducing and evaluating new technologies (e.g. R-IDEAL), and the impact that newly emerging analytical techniques such as causal inference and in-silico trials may have on these approaches in the future. Lastly, we will discuss whether the Leaning Healthcare System concept, that combines digital healthcare initiatives and clinical studies with the continuous improvement approaches used in Quality Improvement, might have a role in the introduction, evaluation, and potential optimisation of technical changes in radiotherapy practice.
    • Testicular seminoma and non-seminoma: ESMO-EURACAN Clinical Practice Guideline for diagnosis, treatment and follow-up

      Oldenburg, J.; Berney, D. M.; Bokemeyer, C.; Climent, M. A.; Daugaard, G.; Gietema, J. A.; De Giorgi, U.; Haugnes, H. S.; Huddart, R. A.; Leao, R.; et al. (2022)
    • Machine learning based models of radiotherapy-induced skin induration for breast cancer patients

      Cicchetti, A.; La Rocca, E.; De Santis, M. C.; Seibold, P.; Azria, D.; De Ruysscher, D.; Valdagni, R.; Dunning, A. M.; Elliot, R.; Gutierrez-Enriquez, S.; et al. (2022)
      Purpose or Objective To use data from an international prospective cohort study of breast cancer patients (pts) to predict the risk of skin induration (SI) after radiotherapy (RT) using a machine learning algorithm that includes dosimetric/clinical/genetic factors. Materials and Methods Pts were treated after breast conserving surgery with conventional/moderate or ultra hypo-fractionated RT with or without a tumour bed boost based on clinical and pathological factors. Pts were enrolled in 7 countries in Europe/US; each centre followed local clinical practice, but the collection of data and genotyping was standardised and centralised. Our endpoint was late grade 1+ (G1+) SI 2 years after RT completion. Inclusion criteria were: no SI at baseline and availability of complete dosimetric and genetic data. For every pt, skin was defined as a 5-mm inner isotropic expansion from the outer body. To select a relevant portion of the skin DVH, we extracted the higher dose tail using different volume cutoffs (i.e. 25/50/100/150/200 cc volumes corresponding to 5x5-20x20cm2 areas). We corrected sub-DHVs for fractionation using two possible a/b values from the literature (1.8 Gy, Bentzen 1988 & Raza 2012; 3.6 Gy, Jones 2006 & Budach 2015). We calculated Equivalent Uniform Doses (EUDs) from corrected sub-DVHs, with n spanning from 1 to 0.05. We also considered the minimum dose of the selected DVH tail as an additional dose parameter (Dmin). Toxicity models were built using feed-forward neural networks (FNNs, 10 neurons and 1 hidden layer) following a wrapper method for feature selection. We used separate datasets for input: clinical/treatment/genetic features were constant, while the dosimetric factors (EUDs and Dmin) coming from sub-DVHs varied with volume cutoff and a/b .Results The 647 pts included in the analysis had a G1+ SI rate at 2 years of 29.4%. 281 variables were considered: 127 published SNPs (GWAS literature), 40 clinical factors, 93 treatment factors and 21 dosimetric variables (for each volume and a/b). For volume thresholds <200cc, no dosimetric feature was selected by the wrapper method. Therefore, we derived a predictive model (16 features, no dosimetric variable) for use before RT planning (Model 1). At sub-DVH_200cc, for a/b=3.6Gy only Dmin was selected (Model 2) as dosimetric variable, while for a/b=1.8Gy, EUD (n=0.5) and Dmin entered the FNN (Model 3). Conclusion A pre-planning SI model was derived that included information on genetics (6 SNPs), treatment (6 RT, 1 oncology) and clinical factors. Largest volume (200cc) sub-DVH allowed selection of dosimetric features, particularly with a/b=1.8Gy and EUD with n=0.5. Following validation, the model could be used to personalise use of new RT schedules, such as ultrahigh hypofractionation, to minimise risk of Sl.
    • The association between Braf-V600e mutations and death from thin (<=1.0 Mm) melanoma: a population-based nested case-case study from Queensland, Australia

      Claeson, M.; Tan, S.; Brown, S.; Walsh, M. D.; Lambie, D.; Baade, P. D.; Whitehead, K. J.; Soyer, H. P.; Smithers, B. M.; Green, Adèle C; et al. (2022)
      Purpose: BRAF mutations are common in cutaneous melanoma but their prognostic significance is unclear, especially for early-stage tumours. We investigated whether BRAF-V600E mutations in thin (≤1.00 mm) melanoma can predict melanoma mortality.Methods: In this REMARK-compliant, nested case-case study, we collected data on a cohort of 27,660 people with a diagnosis of a thin (≤1.00 mm) single locally invasive melanoma between 1995 and 2014 from the population-based Queensland Cancer Registry, Australia. Within this cohort, 436 (1.6%) were fatal ca-ses, i.e. people who had died from their melanoma. We retrieved archival tumours for 85 of these fatal cases which were randomly matched (1:1) with 85 non-fatal cases (melanoma survivors) by age, sex, year of diagnosis, follow-up interval, and tumour thick-ness. BRAF-V600E mutation status in the melanoma tissue was analysed with immunohistochemistry. Using conditional logistic regression, we calculated odds ratios (ORs) for melanoma-specific mortality, adjusting for anatomical location.Results: BRAF-V600E mutations were present in 19 of 85 (22%) fatal cases and 29 of 85 (34%) non-fatal cases. People with BRAF-V600E mutations were more commonly women (52% vs. 17%) and younger (median 52 vs. 65 years) than those with wild-type tumours. Preliminary analyses show that BRAF-V600E mutations were associated with lower melanoma-specific mortality (OR 0.30, 95% CI 0.10–0.89), after adjusting for anatomical site.Conclusions: We found BRAF-V600E mutations to be inversely associated with death from thin (≤1.00 mm) melanoma. Identifica-tion of people with potentially fatal thin melanomas would produce an opportunity to intensify follow-up post-diagnosis
    • Polygenic risk score as a determinant of risk of keratinocyte cancer in an Australian population-based cohort

      Liyanage, U. E.; Law, M. H.; Antonsson, A.; Hughes, M. C. B.; Gordon, S.; van der Pols, J. C.; Green, Adèle C; QIMR Berghofer Medical Research Institute, Brisbane, Australia (2022)
      Background: Keratinocyte cancer (KC) risk is determined by genetic and environmental factors. Genetic risk can be quantified by polygenic risk scores (PRS), which sum the combined effects of single nucleotide polymorphisms (SNPs). Objectives: Our objective here was to evaluate the contribution of the summed genetic score to predict the KC risk in the phenotypically well-characterised Nambour population. Methods: We used PLINK v1.90 to calculate PRS for 432 cases, 566 controls, using 78 genome-wide independent SNPs that are associated with KC risk. We assessed the association between PRS and KC using logistic regression, stratifying the cohort into 3 risk groups (high 20%, intermediate 60%, low 20%). Results: The fully adjusted model including traditional risk factors (phenotypic and sun exposure-related), showed a significant 50% increase in odds of KC per standard deviation of PRS (odds ratio (OR) =1.51; 95% confidence interval (CI) =1.30-1.76, P=5.75 × 10-8 ). Those in the top 20% PRS had over three times the risk of KC of those in the lowest 20% (OR=3.45; 95% CI=2.18-5.50, P=1.5×10-7 ) and higher absolute risk of KC per 100 person-years of 2.96 compared with 1.34. Area under the ROC curve increased from 0.72 to 0.74 on adding PRS to the fully adjusted model. Conclusions: These results show that PRS can enhance the prediction of KC above traditional risk factors.